Overview

Dataset statistics

Number of variables16
Number of observations27
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory132.7 B

Variable types

Categorical2
Numeric14

Alerts

Casos_entre_nascidos is highly overall correlated with Espinha bífidaprop and 10 other fieldsHigh correlation
Espinha bífidaprop is highly overall correlated with Casos_entre_nascidos and 8 other fieldsHigh correlation
Outras malformações congênitas do sistema nervosoprop is highly overall correlated with UF and 1 other fieldsHigh correlation
Malformações congênitas do aparelho circulatórioprop is highly overall correlated with Casos_entre_nascidos and 7 other fieldsHigh correlation
Fenda labial e fenda palatinaprop is highly overall correlated with Casos_entre_nascidos and 3 other fieldsHigh correlation
Ausência atresia e estenose do intestino delgadoprop is highly overall correlated with UF and 1 other fieldsHigh correlation
Outras malformações congênitas do aparelho digestivoprop is highly overall correlated with Deformidades congênitas do quadrilprop and 3 other fieldsHigh correlation
Testiculo não-descidoprop is highly overall correlated with Casos_entre_nascidos and 6 other fieldsHigh correlation
Outras malformações do aparelho geniturinárioprop is highly overall correlated with Casos_entre_nascidos and 8 other fieldsHigh correlation
Deformidades congênitas do quadrilprop is highly overall correlated with Outras malformações congênitas do aparelho digestivoprop and 2 other fieldsHigh correlation
Deformidades congênitas dos pésprop is highly overall correlated with Casos_entre_nascidos and 6 other fieldsHigh correlation
Outras malformações e deformidades congênitas do aparelho osteomuscularprop is highly overall correlated with Casos_entre_nascidos and 8 other fieldsHigh correlation
Outras malformações congênitasprop is highly overall correlated with Casos_entre_nascidos and 7 other fieldsHigh correlation
Anomalias cromossômicas não classificadas em outra parteprop is highly overall correlated with Casos_entre_nascidos and 4 other fieldsHigh correlation
UF is highly overall correlated with Casos_entre_nascidos and 14 other fieldsHigh correlation
Estado is highly overall correlated with Casos_entre_nascidos and 14 other fieldsHigh correlation
UF is uniformly distributedUniform
Estado is uniformly distributedUniform
UF has unique valuesUnique
Estado has unique valuesUnique
Casos_entre_nascidos has unique valuesUnique
Espinha bífidaprop has unique valuesUnique
Outras malformações congênitas do sistema nervosoprop has unique valuesUnique
Malformações congênitas do aparelho circulatórioprop has unique valuesUnique
Fenda labial e fenda palatinaprop has unique valuesUnique
Outras malformações congênitas do aparelho digestivoprop has unique valuesUnique
Outras malformações do aparelho geniturinárioprop has unique valuesUnique
Deformidades congênitas dos pésprop has unique valuesUnique
Outras malformações e deformidades congênitas do aparelho osteomuscularprop has unique valuesUnique
Outras malformações congênitasprop has unique valuesUnique
Anomalias cromossômicas não classificadas em outra parteprop has unique valuesUnique
Espinha bífidaprop has 1 (3.7%) zerosZeros
Ausência atresia e estenose do intestino delgadoprop has 12 (44.4%) zerosZeros
Testiculo não-descidoprop has 2 (7.4%) zerosZeros
Deformidades congênitas do quadrilprop has 6 (22.2%) zerosZeros

Reproduction

Analysis started2023-04-16 17:07:59.849007
Analysis finished2023-04-16 17:08:19.724263
Duration19.88 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

UF
Categorical

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size344.0 B
AC
 
1
PB
 
1
SP
 
1
SE
 
1
SC
 
1
Other values (22)
22 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters54
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st rowAC
2nd rowAL
3rd rowAM
4th rowAP
5th rowBA

Common Values

ValueCountFrequency (%)
AC 1
 
3.7%
PB 1
 
3.7%
SP 1
 
3.7%
SE 1
 
3.7%
SC 1
 
3.7%
RS 1
 
3.7%
RR 1
 
3.7%
RO 1
 
3.7%
RN 1
 
3.7%
RJ 1
 
3.7%
Other values (17) 17
63.0%

Length

2023-04-16T14:08:19.787959image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ac 1
 
3.7%
al 1
 
3.7%
am 1
 
3.7%
ap 1
 
3.7%
ba 1
 
3.7%
ce 1
 
3.7%
df 1
 
3.7%
es 1
 
3.7%
go 1
 
3.7%
ma 1
 
3.7%
Other values (17) 17
63.0%

Most occurring characters

ValueCountFrequency (%)
A 7
13.0%
P 7
13.0%
R 7
13.0%
S 6
11.1%
M 5
9.3%
E 4
7.4%
O 3
 
5.6%
C 3
 
5.6%
B 2
 
3.7%
G 2
 
3.7%
Other values (7) 8
14.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 54
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 7
13.0%
P 7
13.0%
R 7
13.0%
S 6
11.1%
M 5
9.3%
E 4
7.4%
O 3
 
5.6%
C 3
 
5.6%
B 2
 
3.7%
G 2
 
3.7%
Other values (7) 8
14.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 54
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 7
13.0%
P 7
13.0%
R 7
13.0%
S 6
11.1%
M 5
9.3%
E 4
7.4%
O 3
 
5.6%
C 3
 
5.6%
B 2
 
3.7%
G 2
 
3.7%
Other values (7) 8
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 7
13.0%
P 7
13.0%
R 7
13.0%
S 6
11.1%
M 5
9.3%
E 4
7.4%
O 3
 
5.6%
C 3
 
5.6%
B 2
 
3.7%
G 2
 
3.7%
Other values (7) 8
14.8%

Estado
Categorical

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size344.0 B
Acre
 
1
Paraíba
 
1
São Paulo
 
1
Sergipe
 
1
Santa Catarina
 
1
Other values (22)
22 

Length

Max length19
Median length16
Mean length9.4074074
Min length4

Characters and Unicode

Total characters254
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st rowAcre
2nd rowAlagoas
3rd rowAmazonas
4th rowAmapá
5th rowBahia

Common Values

ValueCountFrequency (%)
Acre 1
 
3.7%
Paraíba 1
 
3.7%
São Paulo 1
 
3.7%
Sergipe 1
 
3.7%
Santa Catarina 1
 
3.7%
Rio Grande do Sul 1
 
3.7%
Roraima 1
 
3.7%
Rondônia 1
 
3.7%
Rio Grande do Norte 1
 
3.7%
Rio de Janeiro 1
 
3.7%
Other values (17) 17
63.0%

Length

2023-04-16T14:08:19.874256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rio 3
 
6.8%
do 3
 
6.8%
sul 2
 
4.5%
grosso 2
 
4.5%
grande 2
 
4.5%
mato 2
 
4.5%
federal 1
 
2.3%
ceará 1
 
2.3%
bahia 1
 
2.3%
amapá 1
 
2.3%
Other values (26) 26
59.1%

Most occurring characters

ValueCountFrequency (%)
a 37
14.6%
o 27
 
10.6%
r 20
 
7.9%
i 17
 
6.7%
17
 
6.7%
n 15
 
5.9%
e 13
 
5.1%
s 12
 
4.7%
t 10
 
3.9%
d 8
 
3.1%
Other values (27) 78
30.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 197
77.6%
Uppercase Letter 40
 
15.7%
Space Separator 17
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 37
18.8%
o 27
13.7%
r 20
10.2%
i 17
8.6%
n 15
7.6%
e 13
 
6.6%
s 12
 
6.1%
t 10
 
5.1%
d 8
 
4.1%
u 5
 
2.5%
Other values (12) 33
16.8%
Uppercase Letter
ValueCountFrequency (%)
G 6
15.0%
S 6
15.0%
P 6
15.0%
R 5
12.5%
M 4
10.0%
A 4
10.0%
C 2
 
5.0%
J 1
 
2.5%
N 1
 
2.5%
E 1
 
2.5%
Other values (4) 4
10.0%
Space Separator
ValueCountFrequency (%)
17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 237
93.3%
Common 17
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 37
15.6%
o 27
 
11.4%
r 20
 
8.4%
i 17
 
7.2%
n 15
 
6.3%
e 13
 
5.5%
s 12
 
5.1%
t 10
 
4.2%
d 8
 
3.4%
G 6
 
2.5%
Other values (26) 72
30.4%
Common
ValueCountFrequency (%)
17
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 243
95.7%
None 11
 
4.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 37
15.2%
o 27
11.1%
r 20
 
8.2%
i 17
 
7.0%
17
 
7.0%
n 15
 
6.2%
e 13
 
5.3%
s 12
 
4.9%
t 10
 
4.1%
d 8
 
3.3%
Other values (23) 67
27.6%
None
ValueCountFrequency (%)
á 5
45.5%
í 3
27.3%
ã 2
 
18.2%
ô 1
 
9.1%

Casos_entre_nascidos
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0076539137
Minimum0.0048831557
Maximum0.013712724
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:08:19.946840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.0048831557
5-th percentile0.0049643129
Q10.0064346303
median0.0073375683
Q30.0086658123
95-th percentile0.011407935
Maximum0.013712724
Range0.0088295685
Interquartile range (IQR)0.002231182

Descriptive statistics

Standard deviation0.0020501995
Coefficient of variation (CV)0.2678629
Kurtosis2.0492482
Mean0.0076539137
Median Absolute Deviation (MAD)0.001119046
Skewness1.1785442
Sum0.20665567
Variance4.2033182 × 10-6
MonotonicityNot monotonic
2023-04-16T14:08:20.035296image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.007925791111 1
 
3.7%
0.009128833499 1
 
3.7%
0.01371272414 1
 
3.7%
0.01206468726 1
 
3.7%
0.008292717671 1
 
3.7%
0.009201457545 1
 
3.7%
0.005892399658 1
 
3.7%
0.008029647931 1
 
3.7%
0.008456614357 1
 
3.7%
0.006775547127 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.004883155675 1
3.7%
0.004940787946 1
3.7%
0.005019204303 1
3.7%
0.005388100695 1
3.7%
0.005892399658 1
3.7%
0.00597881193 1
3.7%
0.006393313001 1
3.7%
0.00647594754 1
3.7%
0.006647506208 1
3.7%
0.006775547127 1
3.7%
ValueCountFrequency (%)
0.01371272414 1
3.7%
0.01206468726 1
3.7%
0.009875514426 1
3.7%
0.009201457545 1
3.7%
0.009128833499 1
3.7%
0.009080741164 1
3.7%
0.008875010272 1
3.7%
0.008456614357 1
3.7%
0.008292717671 1
3.7%
0.008029647931 1
3.7%

Espinha bífidaprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.024043572
Minimum0
Maximum0.048486923
Zeros1
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:08:20.119294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0064899484
Q10.015282065
median0.02211322
Q30.033511611
95-th percentile0.044226107
Maximum0.048486923
Range0.048486923
Interquartile range (IQR)0.018229546

Descriptive statistics

Standard deviation0.012558885
Coefficient of variation (CV)0.52233855
Kurtosis-0.75295092
Mean0.024043572
Median Absolute Deviation (MAD)0.0098675363
Skewness0.11980119
Sum0.64917645
Variance0.00015772558
MonotonicityNot monotonic
2023-04-16T14:08:20.207108image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.03522573827 1
 
3.7%
0.03490949713 1
 
3.7%
0.03652378367 1
 
3.7%
0.04848692279 1
 
3.7%
0.01933271604 1
 
3.7%
0.03107169086 1
 
3.7%
0 1
 
3.7%
0.03996657341 1
 
3.7%
0.03211372541 1
 
3.7%
0.03089971922 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0 1
3.7%
0.005954153022 1
3.7%
0.007740137774 1
3.7%
0.01053050336 1
3.7%
0.0122456834 1
3.7%
0.01391003866 1
3.7%
0.01483041421 1
3.7%
0.0157337156 1
3.7%
0.01581384841 1
3.7%
0.01621241871 1
3.7%
ValueCountFrequency (%)
0.04848692279 1
3.7%
0.04605162167 1
3.7%
0.03996657341 1
3.7%
0.03697920947 1
3.7%
0.03652378367 1
3.7%
0.03522573827 1
3.7%
0.03490949713 1
3.7%
0.03211372541 1
3.7%
0.03206807818 1
3.7%
0.03107169086 1
3.7%

Outras malformações congênitas do sistema nervosoprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.078602787
Minimum0.038486434
Maximum0.14169967
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:08:20.289606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.038486434
5-th percentile0.048449291
Q10.062666611
median0.077072941
Q30.085904631
95-th percentile0.10438237
Maximum0.14169967
Range0.10321324
Interquartile range (IQR)0.02323802

Descriptive statistics

Standard deviation0.021606709
Coefficient of variation (CV)0.27488477
Kurtosis1.5588057
Mean0.078602787
Median Absolute Deviation (MAD)0.013987236
Skewness0.7287269
Sum2.1222753
Variance0.00046684988
MonotonicityNot monotonic
2023-04-16T14:08:20.374531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.04696765103 1
 
3.7%
0.07505541883 1
 
3.7%
0.1038645098 1
 
3.7%
0.08271298594 1
 
3.7%
0.06308570498 1
 
3.7%
0.06073103215 1
 
3.7%
0.0683176772 1
 
3.7%
0.1416996694 1
 
3.7%
0.07707294097 1
 
3.7%
0.05866468431 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.03848643353 1
3.7%
0.04696765103 1
3.7%
0.05190644975 1
3.7%
0.05866468431 1
3.7%
0.05934105627 1
3.7%
0.06073103215 1
3.7%
0.06224751645 1
3.7%
0.06308570498 1
3.7%
0.0683176772 1
3.7%
0.07257395632 1
3.7%
ValueCountFrequency (%)
0.1416996694 1
3.7%
0.1045352203 1
3.7%
0.104025717 1
3.7%
0.1038645098 1
3.7%
0.1012206014 1
3.7%
0.09907589213 1
3.7%
0.08628482209 1
3.7%
0.08552444025 1
3.7%
0.08391314989 1
3.7%
0.08358748447 1
3.7%

Malformações congênitas do aparelho circulatórioprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.059204828
Minimum0.0095173949
Maximum0.29626658
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:08:20.462353image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.0095173949
5-th percentile0.013366904
Q10.029403058
median0.040145922
Q30.073297981
95-th percentile0.1364964
Maximum0.29626658
Range0.28674919
Interquartile range (IQR)0.043894923

Descriptive statistics

Standard deviation0.057718143
Coefficient of variation (CV)0.97488912
Kurtosis10.874773
Mean0.059204828
Median Absolute Deviation (MAD)0.022549582
Skewness2.9516127
Sum1.5985304
Variance0.003331384
MonotonicityNot monotonic
2023-04-16T14:08:20.546676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.07045147655 1
 
3.7%
0.0401459217 1
 
3.7%
0.2962665845 1
 
3.7%
0.04278257893 1
 
3.7%
0.06919077321 1
 
3.7%
0.1525337552 1
 
3.7%
0.0341588386 1
 
3.7%
0.02906659884 1
 
3.7%
0.02997281038 1
 
3.7%
0.04746913387 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.009517394869 1
3.7%
0.01190830604 1
3.7%
0.01677029851 1
3.7%
0.01759633996 1
3.7%
0.02273996846 1
3.7%
0.02622285934 1
3.7%
0.02906659884 1
3.7%
0.02973951682 1
3.7%
0.02997281038 1
3.7%
0.03148246427 1
3.7%
ValueCountFrequency (%)
0.2962665845 1
3.7%
0.1525337552 1
3.7%
0.09907589213 1
3.7%
0.09861122524 1
3.7%
0.08409363192 1
3.7%
0.07812698493 1
3.7%
0.0743040836 1
3.7%
0.07229187846 1
3.7%
0.07045147655 1
3.7%
0.06919077321 1
3.7%

Fenda labial e fenda palatinaprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.063580865
Minimum0.034987667
Maximum0.11263307
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:08:20.634591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.034987667
5-th percentile0.036647511
Q10.050752414
median0.060842908
Q30.076990847
95-th percentile0.093609275
Maximum0.11263307
Range0.077645404
Interquartile range (IQR)0.026238433

Descriptive statistics

Standard deviation0.019152021
Coefficient of variation (CV)0.30122304
Kurtosis0.20271627
Mean0.063580865
Median Absolute Deviation (MAD)0.015957985
Skewness0.58409369
Sum1.7166834
Variance0.00036679993
MonotonicityNot monotonic
2023-04-16T14:08:20.707241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.05283860741 1
 
3.7%
0.07680089368 1
 
3.7%
0.08022188199 1
 
3.7%
0.07700864208 1
 
3.7%
0.07936588691 1
 
3.7%
0.07697305237 1
 
3.7%
0.05977796755 1
 
3.7%
0.1126330705 1
 
3.7%
0.06636836584 1
 
3.7%
0.05418646413 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.03498766685 1
3.7%
0.03519267992 1
3.7%
0.04004211838 1
3.7%
0.04167907115 1
3.7%
0.043175951 1
3.7%
0.04683587628 1
3.7%
0.05025731747 1
3.7%
0.05124751084 1
3.7%
0.05240393264 1
3.7%
0.05283860741 1
3.7%
ValueCountFrequency (%)
0.1126330705 1
3.7%
0.09861122524 1
3.7%
0.08193805737 1
3.7%
0.0804075934 1
3.7%
0.08022188199 1
3.7%
0.07936588691 1
3.7%
0.07700864208 1
3.7%
0.07697305237 1
3.7%
0.07680089368 1
3.7%
0.07611135478 1
3.7%

Ausência atresia e estenose do intestino delgadoprop
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)59.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0016396134
Minimum0
Maximum0.0071225796
Zeros12
Zeros (%)44.4%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:08:20.796642image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.00098869428
Q30.003108903
95-th percentile0.004307491
Maximum0.0071225796
Range0.0071225796
Interquartile range (IQR)0.003108903

Descriptive statistics

Standard deviation0.0019297367
Coefficient of variation (CV)1.1769462
Kurtosis0.70329225
Mean0.0016396134
Median Absolute Deviation (MAD)0.00098869428
Skewness1.0803242
Sum0.044269561
Variance3.7238836 × 10-6
MonotonicityNot monotonic
2023-04-16T14:08:20.877179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 12
44.4%
0.002010292699 1
 
3.7%
0.0009886942809 1
 
3.7%
0.001564296497 1
 
3.7%
0.003602759714 1
 
3.7%
0.0008994989791 1
 
3.7%
0.001535956747 1
 
3.7%
0.004392643786 1
 
3.7%
0.001461956244 1
 
3.7%
0.003910297769 1
 
3.7%
Other values (6) 6
22.2%
ValueCountFrequency (%)
0 12
44.4%
0.0008994989791 1
 
3.7%
0.0009886942809 1
 
3.7%
0.001461956244 1
 
3.7%
0.001535956747 1
 
3.7%
0.001564296497 1
 
3.7%
0.002010292699 1
 
3.7%
0.002540714957 1
 
3.7%
0.002686932106 1
 
3.7%
0.003530873962 1
 
3.7%
ValueCountFrequency (%)
0.007122579595 1
3.7%
0.004392643786 1
3.7%
0.004108801052 1
3.7%
0.003913262536 1
3.7%
0.003910297769 1
3.7%
0.003602759714 1
3.7%
0.003530873962 1
3.7%
0.002686932106 1
3.7%
0.002540714957 1
3.7%
0.002010292699 1
3.7%

Outras malformações congênitas do aparelho digestivoprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.050345994
Minimum0.022739968
Maximum0.19648705
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:08:20.973261image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.022739968
5-th percentile0.025081602
Q10.035062148
median0.041956575
Q30.051417678
95-th percentile0.101025
Maximum0.19648705
Range0.17374708
Interquartile range (IQR)0.01635553

Descriptive statistics

Standard deviation0.034407241
Coefficient of variation (CV)0.68341568
Kurtosis13.219141
Mean0.050345994
Median Absolute Deviation (MAD)0.0089189936
Skewness3.4173617
Sum1.3593418
Variance0.0011838583
MonotonicityNot monotonic
2023-04-16T14:08:21.046526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.05870956379 1
 
3.7%
0.05934614512 1
 
3.7%
0.1187022969 1
 
3.7%
0.04563475086 1
 
3.7%
0.05087556853 1
 
3.7%
0.04519518671 1
 
3.7%
0.05977796755 1
 
3.7%
0.03633324856 1
 
3.7%
0.05780470573 1
 
3.7%
0.02731714307 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.02273996846 1
3.7%
0.02412351238 1
3.7%
0.02731714307 1
3.7%
0.02855218461 1
3.7%
0.02967052814 1
3.7%
0.03143205924 1
3.7%
0.03379104844 1
3.7%
0.03633324856 1
3.7%
0.03714782881 1
3.7%
0.0386784561 1
3.7%
ValueCountFrequency (%)
0.1964870497 1
3.7%
0.1187022969 1
3.7%
0.05977796755 1
3.7%
0.05934614512 1
3.7%
0.05870956379 1
3.7%
0.05780470573 1
3.7%
0.05195978764 1
3.7%
0.05087556853 1
3.7%
0.04797229308 1
3.7%
0.04723335024 1
3.7%

Testiculo não-descidoprop
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.013486946
Minimum0
Maximum0.048873296
Zeros2
Zeros (%)7.4%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:08:21.161144image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.00038700689
Q10.0050736384
median0.0087409531
Q30.018535294
95-th percentile0.040215463
Maximum0.048873296
Range0.048873296
Interquartile range (IQR)0.013461655

Descriptive statistics

Standard deviation0.012640148
Coefficient of variation (CV)0.93721352
Kurtosis1.7632358
Mean0.013486946
Median Absolute Deviation (MAD)0.0052541136
Skewness1.4591149
Sum0.36414753
Variance0.00015977333
MonotonicityNot monotonic
2023-04-16T14:08:21.242776image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 2
 
7.4%
0.008041170794 1
 
3.7%
0.04337199311 1
 
3.7%
0.0199652035 1
 
3.7%
0.0172976933 1
 
3.7%
0.01977289419 1
 
3.7%
0.003633324856 1
 
3.7%
0.004281830054 1
 
3.7%
0.01298683851 1
 
3.7%
0.01143321731 1
 
3.7%
Other values (16) 16
59.3%
ValueCountFrequency (%)
0 2
7.4%
0.001290022962 1
3.7%
0.002928429191 1
3.7%
0.003597995916 1
3.7%
0.003633324856 1
3.7%
0.004281830054 1
3.7%
0.005865446654 1
3.7%
0.005954153022 1
3.7%
0.006777363606 1
3.7%
0.008041170794 1
3.7%
ValueCountFrequency (%)
0.04887329598 1
3.7%
0.04337199311 1
3.7%
0.03285022643 1
3.7%
0.02777716863 1
3.7%
0.025219318 1
3.7%
0.0199652035 1
3.7%
0.01977289419 1
3.7%
0.0172976933 1
3.7%
0.01404067114 1
3.7%
0.01399506674 1
3.7%

Outras malformações do aparelho geniturinárioprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.079205144
Minimum0.011908306
Maximum0.15686946
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:08:21.329366image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.011908306
5-th percentile0.030213554
Q10.056017762
median0.072340509
Q30.098050594
95-th percentile0.13809658
Maximum0.15686946
Range0.14496115
Interquartile range (IQR)0.042032832

Descriptive statistics

Standard deviation0.036364511
Coefficient of variation (CV)0.45911805
Kurtosis-0.30396464
Mean0.079205144
Median Absolute Deviation (MAD)0.019952985
Skewness0.42870578
Sum2.1385389
Variance0.0013223777
MonotonicityNot monotonic
2023-04-16T14:08:21.404949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.0821933893 1
 
3.7%
0.1396379885 1
 
3.7%
0.1333770314 1
 
3.7%
0.1568694561 1
 
3.7%
0.1119262508 1
 
3.7%
0.1038076945 1
 
3.7%
0.04269854825 1
 
3.7%
0.0653998474 1
 
3.7%
0.08135477103 1
 
3.7%
0.07209934484 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.01190830604 1
3.7%
0.02967052814 1
3.7%
0.0314806138 1
3.7%
0.04269854825 1
3.7%
0.04370476557 1
3.7%
0.05195978764 1
3.7%
0.05486943772 1
3.7%
0.05716608654 1
3.7%
0.06122841698 1
3.7%
0.0653998474 1
3.7%
ValueCountFrequency (%)
0.1568694561 1
3.7%
0.1396379885 1
3.7%
0.1344999744 1
3.7%
0.1333770314 1
3.7%
0.1273728326 1
3.7%
0.1119262508 1
3.7%
0.1038076945 1
3.7%
0.09229349331 1
3.7%
0.08466485328 1
3.7%
0.0821933893 1
3.7%

Deformidades congênitas do quadrilprop
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)81.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0054076627
Minimum0
Maximum0.030164732
Zeros6
Zeros (%)22.2%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:08:21.492652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0010012324
median0.0036333249
Q30.0065172748
95-th percentile0.024120839
Maximum0.030164732
Range0.030164732
Interquartile range (IQR)0.0055160424

Descriptive statistics

Standard deviation0.0076410786
Coefficient of variation (CV)1.4130095
Kurtosis6.7545032
Mean0.0054076627
Median Absolute Deviation (MAD)0.0029012176
Skewness2.595309
Sum0.14600689
Variance5.8386082 × 10-5
MonotonicityNot monotonic
2023-04-16T14:08:21.571253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 6
22.2%
0.02935478189 1
 
3.7%
0.006981899426 1
 
3.7%
0.005704343858 1
 
3.7%
0.007122579595 1
 
3.7%
0.002118524377 1
 
3.7%
0.003633324856 1
 
3.7%
0.002140915027 1
 
3.7%
0.00179128807 1
 
3.7%
0.001270357479 1
 
3.7%
Other values (12) 12
44.4%
ValueCountFrequency (%)
0 6
22.2%
0.0007321072976 1
 
3.7%
0.001270357479 1
 
3.7%
0.00179128807 1
 
3.7%
0.001977388562 1
 
3.7%
0.002118524377 1
 
3.7%
0.002140915027 1
 
3.7%
0.002303935121 1
 
3.7%
0.003633324856 1
 
3.7%
0.003654890609 1
 
3.7%
ValueCountFrequency (%)
0.03016473205 1
3.7%
0.02935478189 1
3.7%
0.01190830604 1
3.7%
0.007122579595 1
3.7%
0.006997533369 1
3.7%
0.006981899426 1
3.7%
0.006777363606 1
3.7%
0.006257185987 1
3.7%
0.005850279643 1
3.7%
0.005704343858 1
3.7%

Deformidades congênitas dos pésprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11103329
Minimum0.054230884
Maximum0.17398249
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:08:21.651749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.054230884
5-th percentile0.069751842
Q10.084740929
median0.10310152
Q30.13501164
95-th percentile0.17045347
Maximum0.17398249
Range0.1197516
Interquartile range (IQR)0.050270708

Descriptive statistics

Standard deviation0.03364415
Coefficient of variation (CV)0.30300958
Kurtosis-0.75377601
Mean0.11103329
Median Absolute Deviation (MAD)0.02306989
Skewness0.45028307
Sum2.9978988
Variance0.0011319288
MonotonicityNot monotonic
2023-04-16T14:08:21.865171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.1115481712 1
 
3.7%
0.1396379885 1
 
3.7%
0.1353336627 1
 
3.7%
0.1739824877 1
 
3.7%
0.12617141 1
 
3.7%
0.1031015197 1
 
3.7%
0.09393680615 1
 
3.7%
0.09083312139 1
 
3.7%
0.1648504571 1
 
3.7%
0.07299498887 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.05423088361 1
3.7%
0.06836192241 1
3.7%
0.07299498887 1
3.7%
0.07745933385 1
3.7%
0.08126391004 1
3.7%
0.08132836327 1
3.7%
0.0830503196 1
3.7%
0.08643153848 1
3.7%
0.08740953114 1
3.7%
0.09083312139 1
3.7%
ValueCountFrequency (%)
0.1739824877 1
3.7%
0.1728547629 1
3.7%
0.1648504571 1
3.7%
0.1643520421 1
3.7%
0.1410787775 1
3.7%
0.1396379885 1
3.7%
0.1353336627 1
3.7%
0.1346896108 1
3.7%
0.1329501242 1
3.7%
0.12617141 1
3.7%
Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24837774
Minimum0.13663535
Maximum0.49057357
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:08:21.945152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.13663535
5-th percentile0.14665739
Q10.19719231
median0.25039197
Q30.27111321
95-th percentile0.37952203
Maximum0.49057357
Range0.35393822
Interquartile range (IQR)0.073920902

Descriptive statistics

Standard deviation0.075988751
Coefficient of variation (CV)0.30594026
Kurtosis3.0870779
Mean0.24837774
Median Absolute Deviation (MAD)0.030688116
Skewness1.2817076
Sum6.706199
Variance0.0057742903
MonotonicityNot monotonic
2023-04-16T14:08:22.030116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.2113544296 1
 
3.7%
0.2635667033 1
 
3.7%
0.3440409979 1
 
3.7%
0.4905735718 1
 
3.7%
0.2452202403 1
 
3.7%
0.2690525959 1
 
3.7%
0.1366353544 1
 
3.7%
0.2724993642 1
 
3.7%
0.2547688882 1
 
3.7%
0.2624237023 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.1366353544 1
3.7%
0.1406125029 1
3.7%
0.1607621316 1
3.7%
0.1661883566 1
3.7%
0.1745028019 1
3.7%
0.1854346343 1
3.7%
0.1888045873 1
3.7%
0.2055800294 1
3.7%
0.2113544296 1
3.7%
0.2312050611 1
3.7%
ValueCountFrequency (%)
0.4905735718 1
3.7%
0.3947281858 1
3.7%
0.3440409979 1
3.7%
0.2958336757 1
3.7%
0.2810800848 1
3.7%
0.2724993642 1
3.7%
0.2723852744 1
3.7%
0.2698411457 1
3.7%
0.2690525959 1
3.7%
0.2635667033 1
3.7%

Outras malformações congênitasprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12282823
Minimum0.059300691
Maximum0.21676507
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:08:22.113150image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.059300691
5-th percentile0.067974653
Q10.092118455
median0.12199254
Q30.15213526
95-th percentile0.19923135
Maximum0.21676507
Range0.15746438
Interquartile range (IQR)0.060016803

Descriptive statistics

Standard deviation0.040951397
Coefficient of variation (CV)0.33340379
Kurtosis-0.10490482
Mean0.12282823
Median Absolute Deviation (MAD)0.03054121
Skewness0.62773406
Sum3.3163622
Variance0.0016770169
MonotonicityNot monotonic
2023-04-16T14:08:22.196369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.09393530206 1
 
3.7%
0.1797839102 1
 
3.7%
0.207565967 1
 
3.7%
0.2167650666 1
 
3.7%
0.1109087394 1
 
3.7%
0.1525337552 1
 
3.7%
0.0853970965 1
 
3.7%
0.1380663445 1
 
3.7%
0.156286797 1
 
3.7%
0.101207776 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.05930069111 1
3.7%
0.06549568324 1
3.7%
0.07375891628 1
3.7%
0.0853970965 1
3.7%
0.08571978378 1
3.7%
0.08646623313 1
3.7%
0.09030160737 1
3.7%
0.09393530206 1
3.7%
0.09450242419 1
3.7%
0.101207776 1
3.7%
ValueCountFrequency (%)
0.2167650666 1
3.7%
0.207565967 1
3.7%
0.1797839102 1
3.7%
0.1673939899 1
3.7%
0.1603222085 1
3.7%
0.156286797 1
3.7%
0.1525337552 1
3.7%
0.1517367602 1
3.7%
0.1380663445 1
3.7%
0.1346106779 1
3.7%

Anomalias cromossômicas não classificadas em outra parteprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.041468531
Minimum0.011610207
Maximum0.084298197
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:08:22.296471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.011610207
5-th percentile0.014865667
Q10.026311928
median0.036185269
Q30.05661894
95-th percentile0.078926662
Maximum0.084298197
Range0.07268799
Interquartile range (IQR)0.030307012

Descriptive statistics

Standard deviation0.020717325
Coefficient of variation (CV)0.49959148
Kurtosis-0.64378866
Mean0.041468531
Median Absolute Deviation (MAD)0.014928342
Skewness0.4994913
Sum1.1196503
Variance0.00042920755
MonotonicityNot monotonic
2023-04-16T14:08:22.390709image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.0821933893 1
 
3.7%
0.03665497199 1
 
3.7%
0.08429819713 1
 
3.7%
0.07130429822 1
 
3.7%
0.06817326184 1
 
3.7%
0.05861250777 1
 
3.7%
0.05977796755 1
 
3.7%
0.01453329942 1
 
3.7%
0.0471001306 1
 
3.7%
0.03089971922 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.01161020666 1
3.7%
0.01453329942 1
3.7%
0.01564119108 1
3.7%
0.01619098162 1
3.7%
0.02125692704 1
3.7%
0.02342743352 1
3.7%
0.02465280631 1
3.7%
0.02797104996 1
3.7%
0.02925139822 1
3.7%
0.02936857562 1
3.7%
ValueCountFrequency (%)
0.08429819713 1
3.7%
0.0821933893 1
3.7%
0.07130429822 1
3.7%
0.06817326184 1
3.7%
0.05977796755 1
3.7%
0.05944553528 1
3.7%
0.05861250777 1
3.7%
0.05462537158 1
3.7%
0.0535873772 1
3.7%
0.0500574879 1
3.7%

Interactions

2023-04-16T14:08:17.921757image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:00.558381image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:01.799112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:03.005247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:04.424970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:05.725771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:07.123799image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:08.570480image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:09.942612image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:11.282590image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:12.592722image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:13.867517image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:15.219109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:16.555314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:17.999120image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:00.630756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:01.881264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:03.098850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:04.508343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:05.898048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:07.208420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:08.658372image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:10.020299image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:11.355357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:12.673340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:13.943984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:15.305646image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:16.641400image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:18.098861image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:00.708485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:01.951472image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:03.195441image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:04.607862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:05.980874image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:07.303478image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:08.739895image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:10.207227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:11.451041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:12.751575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:14.027279image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:15.393226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:16.738870image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:18.181918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:00.787761image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:02.042137image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:03.296012image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:04.690378image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:06.073147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:07.425812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:08.843593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:10.303246image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:11.530547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:12.852928image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:14.111812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:15.493909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:16.823804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:18.273119image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:00.874512image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:02.123226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:03.383069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:04.779393image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:06.164210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:07.531688image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:08.931517image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:10.391136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:11.625630image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:12.941875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:14.206575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:15.590281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:16.906013image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:18.372694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:00.950117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:02.208191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:03.475058image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:04.873395image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:06.249464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:07.625452image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:09.033715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:10.473485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:11.726618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:13.037825image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:14.286934image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:15.676950image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:16.998848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:18.472746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:01.042122image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:02.301084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:03.551798image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:04.974277image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:06.356711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:07.705301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:09.147391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:10.582795image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:11.826387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:13.122924image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:14.376918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:15.782464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:17.122707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:18.570486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:01.127327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:02.390859image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:03.681569image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:05.088964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:06.448421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:07.821805image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:09.255245image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:10.677410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:11.923404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:13.222348image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:14.578303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:15.874557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:17.225425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:18.653437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:01.209665image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:02.455368image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:03.781267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:05.174026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:06.553530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:07.906575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:09.346432image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:10.757987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:12.010636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:13.306897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:14.648697image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:15.972990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:17.329831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:18.872023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:01.305254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:02.557477image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:03.887222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:05.261956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:06.659071image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:08.004589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:09.455197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:10.844382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:12.100333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:13.394404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:14.753858image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:16.071580image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:17.424670image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:18.947288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:01.390133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:02.639815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:03.992754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:05.355905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:06.754894image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:08.099786image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:09.549943image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:10.929700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:12.192906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:13.487290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:14.838002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:16.168044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:17.530857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:19.043308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:01.464510image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:02.724376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:04.094702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:05.438617image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:06.843308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:08.208785image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:09.646394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:11.014482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:12.287944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:13.575068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:14.925869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:16.260711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:17.623893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:19.140589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:01.624446image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:02.803855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:04.209804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:05.530011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:06.931443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:08.349460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:09.754737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:11.110169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:12.392945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:13.672882image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:15.024916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:16.356077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:17.720727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:19.239329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:01.718744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:02.899657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:04.315888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:05.635399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:07.026739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:08.456823image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:09.851611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:11.194165image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:12.493299image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:13.773165image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:15.122791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:16.457765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:08:17.823469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-16T14:08:22.490044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Casos_entre_nascidosEspinha bífidapropOutras malformações congênitas do sistema nervosopropMalformações congênitas do aparelho circulatóriopropFenda labial e fenda palatinapropAusência atresia e estenose do intestino delgadopropOutras malformações congênitas do aparelho digestivopropTesticulo não-descidopropOutras malformações do aparelho geniturináriopropDeformidades congênitas do quadrilpropDeformidades congênitas dos péspropOutras malformações e deformidades congênitas do aparelho osteomuscularpropOutras malformações congênitaspropAnomalias cromossômicas não classificadas em outra partepropUFEstado
Casos_entre_nascidos1.0000.8270.3520.6370.5920.1940.2890.6410.8880.3810.7750.8120.7110.5621.0001.000
Espinha bífidaprop0.8271.0000.2740.5150.5900.179-0.0130.3780.7670.1810.6150.8060.6630.2581.0001.000
Outras malformações congênitas do sistema nervosoprop0.3520.2741.0000.1090.1760.0650.0790.1830.1810.1300.3770.2990.279-0.0121.0001.000
Malformações congênitas do aparelho circulatórioprop0.6370.5150.1091.0000.4540.4010.2080.5490.6100.1410.2790.5020.2830.6031.0001.000
Fenda labial e fenda palatinaprop0.5920.5900.1760.4541.0000.0700.1210.2510.4140.0740.4360.4990.4960.1911.0001.000
Ausência atresia e estenose do intestino delgadoprop0.1940.1790.0650.4010.0701.000-0.3470.2670.303-0.2790.0660.256-0.0020.0931.0001.000
Outras malformações congênitas do aparelho digestivoprop0.289-0.0130.0790.2080.121-0.3471.0000.1440.1670.5750.314-0.1570.1180.6141.0001.000
Testiculo não-descidoprop0.6410.3780.1830.5490.2510.2670.1441.0000.6920.3800.3380.6040.5610.3971.0001.000
Outras malformações do aparelho geniturinárioprop0.8880.7670.1810.6100.4140.3030.1670.6921.0000.2780.6970.7820.6560.4571.0001.000
Deformidades congênitas do quadrilprop0.3810.1810.1300.1410.074-0.2790.5750.3800.2781.0000.3120.1710.2910.4591.0001.000
Deformidades congênitas dos pésprop0.7750.6150.3770.2790.4360.0660.3140.3380.6970.3121.0000.5670.6720.3941.0001.000
Outras malformações e deformidades congênitas do aparelho osteomuscularprop0.8120.8060.2990.5020.4990.256-0.1570.6040.7820.1710.5671.0000.6820.1721.0001.000
Outras malformações congênitasprop0.7110.6630.2790.2830.496-0.0020.1180.5610.6560.2910.6720.6821.0000.1881.0001.000
Anomalias cromossômicas não classificadas em outra parteprop0.5620.258-0.0120.6030.1910.0930.6140.3970.4570.4590.3940.1720.1881.0001.0001.000
UF1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
Estado1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-04-16T14:08:19.379514image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-16T14:08:19.601131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

UFEstadoCasos_entre_nascidosEspinha bífidapropOutras malformações congênitas do sistema nervosopropMalformações congênitas do aparelho circulatóriopropFenda labial e fenda palatinapropAusência atresia e estenose do intestino delgadopropOutras malformações congênitas do aparelho digestivopropTesticulo não-descidopropOutras malformações do aparelho geniturináriopropDeformidades congênitas do quadrilpropDeformidades congênitas dos péspropOutras malformações e deformidades congênitas do aparelho osteomuscularpropOutras malformações congênitaspropAnomalias cromossômicas não classificadas em outra parteprop
0ACAcre0.0079260.0352260.0469680.0704510.0528390.0000000.0587100.0000000.0821930.0293550.1115480.2113540.0939350.082193
1ALAlagoas0.0073380.0221130.1045350.0341750.0502570.0020100.0241240.0080410.0804120.0040210.1346900.2512870.1226280.036185
2AMAmazonas0.0049410.0077400.0593410.0167700.0761110.0000000.0296710.0012900.0296710.0000000.0864320.1406130.0903020.011610
3APAmapá0.0070260.0059540.1012210.0119080.0416790.0000000.1964870.0059540.0119080.0119080.1071750.1607620.0654960.053587
4BABahia0.0064760.0148300.0519060.0227400.0400420.0009890.0227400.0084040.0677260.0019770.0830500.2723850.1077680.021257
5CECeará0.0090810.0320680.1040260.0743040.0524040.0015640.0453650.0328500.0922930.0062570.1728550.2698410.1517370.050057
6DFDistrito Federal0.0053880.0122460.0384860.0297400.0349880.0000000.0472330.0139950.0612280.0069980.0542310.1854350.0857200.033238
7ESEspírito Santo0.0075300.0162120.0990760.0990760.0468360.0036030.0414320.0252190.0846650.0000000.0774590.2449880.0864660.059446
8GOGoiás0.0071840.0105310.0807340.0315920.0608430.0000000.0479720.0140410.0772240.0058500.1111550.2503920.1228560.029251
9MAMaranhão0.0050190.0179900.0782560.0314820.0431760.0008990.0386780.0035980.0548690.0000000.0683620.1745030.0737590.016191
UFEstadoCasos_entre_nascidosEspinha bífidapropOutras malformações congênitas do sistema nervosopropMalformações congênitas do aparelho circulatóriopropFenda labial e fenda palatinapropAusência atresia e estenose do intestino delgadopropOutras malformações congênitas do aparelho digestivopropTesticulo não-descidopropOutras malformações do aparelho geniturináriopropDeformidades congênitas do quadrilpropDeformidades congênitas dos péspropOutras malformações e deformidades congênitas do aparelho osteomuscularpropOutras malformações congênitaspropAnomalias cromossômicas não classificadas em outra parteprop
17PRParaná0.0071900.0260420.0622480.0781270.0819380.0025410.0400160.0114330.0571660.0012700.1022640.2312050.1257650.054625
18RJRio de Janeiro0.0067760.0309000.0586650.0474690.0541860.0026870.0273170.0129870.0720990.0017910.0729950.2624240.1012080.030900
19RNRio Grande do Norte0.0084570.0321140.0770730.0299730.0663680.0000000.0578050.0042820.0813550.0021410.1648500.2547690.1562870.047100
20RORondônia0.0080300.0399670.1417000.0290670.1126330.0000000.0363330.0036330.0654000.0036330.0908330.2724990.1380660.014533
21RRRoraima0.0058920.0000000.0683180.0341590.0597780.0000000.0597780.0000000.0426990.0000000.0939370.1366350.0853970.059778
22RSRio Grande do Sul0.0092010.0310720.0607310.1525340.0769730.0035310.0451950.0197730.1038080.0021190.1031020.2690530.1525340.058613
23SCSanta Catarina0.0082930.0193330.0630860.0691910.0793660.0071230.0508760.0172980.1119260.0071230.1261710.2452200.1109090.068173
24SESergipe0.0120650.0484870.0827130.0427830.0770090.0000000.0456350.0199650.1568690.0057040.1739820.4905740.2167650.071304
25SPSão Paulo0.0137130.0365240.1038650.2962670.0802220.0039130.1187020.0433720.1333770.0301650.1353340.3440410.2075660.084298
26TOTocantins0.0088750.0369790.0862850.0986110.0986110.0041090.0410880.0082180.1273730.0000000.1643520.2958340.0945020.024653